双目摄像头测距——用matlab标定摄像头参数
使用官方标定板（棋盘），测量单个棋盘边长
ps:棋盘一定要固定在一个平面上，不要弯曲卷折。
棋盘图片下载网址：http://docs.opencv.org/2.4/_downloads/pattern.png
1. matlab命令行调用stereoCameraCalibrator工具
2. 添加图片（分成两个文件夹：左和右，按数字编号）
然后按测量单个棋盘边长选择尺寸
可能会出现如下界面，表示符合条件的图片数量
3. 点击Calibrate
运行完成后删除误差较大的数据（0.3以下最佳）
删除方法：左键或右键点击柱状图会跳转到照片，右键remove选中照片
4. 点击后默认选项导出参数
5. 数据：
1.平移参数：stereoParams.PoseCamera2.Translation
2.旋转参数：（转置）stereoParams.PoseCamera2.R（RotationOfCamera2）
3.左、右两个摄像头各自的内部参数：（转置）
需matlab命令行输入代码：
% 假设 stereoParams 是导出的标定结果
leftIntrinsicMatrix = stereoParams.CameraParameters1.IntrinsicMatrix;
rightIntrinsicMatrix = stereoParams.CameraParameters2.IntrinsicMatrix;
% 显示内参矩阵
disp('Left Camera Intrinsic Matrix:');
disp(leftIntrinsicMatrix);
disp('Right Camera Intrinsic Matrix:');
disp(rightIntrinsicMatrix);
👆链接：https://blog.csdn.net/2201_75283572/article/details/144636981?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522043dc2f47b55fe83cb95d15937dbee6d%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=043dc2f47b55fe83cb95d15937dbee6d&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2allsobaiduend~default-1-144636981-null-null.142v102pc_search_result_base5&utm_term=IntrinsicMatrix&spm=1018.2226.3001.4187
4.径向畸变 k1 k2 : stereoParams.CameraParameters1.RadialDistortion
5.切向畸变 p1 p2 : stereoParams.CameraParameters1.TangentialDistortion
k1 k2 p1 p2 k3 (k3=0)
源码：（C++环境）
/******************************/
/* 立体匹配和测距 */
/******************************/
include <opencv2/opencv.hpp>
include <iostream>
include <math.h>
using namespace std;
using namespace cv;
const int imageWidth = 640; //摄像头的分辨率
const int imageHeight = 360;
Vec3f point3;
float d;
Size imageSize = Size(imageWidth, imageHeight);
Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat rectifyImageL, rectifyImageR;
Rect validROIL;//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域
Rect validROIR;
Mat mapLx, mapLy, mapRx, mapRy; //映射表
Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
Mat xyz; //三维坐标
Point origin; //鼠标按下的起始点
Rect selection; //定义矩形选框
bool selectObject = false; //是否选择对象
int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
Ptr<StereoBM> bm = StereoBM::create(16, 9);
/*事先标定好的左相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixL = (Mat_<double>(3, 3) << 485.933782310715, -0.292687497648472, 292.720069591521,
0, 485.273502858935, 214.517060356564,
0, 0, 1);
//获得的畸变参数 左相机畸变参数
/*418.523322187048 0 0
-1.26842201390676 421.222568242056 0
344.758267538961 243.318992284899 1 */ //2
Mat distCoeffL = (Mat_<double>(5, 1) << 0.127909393166272, -0.144506182077827, -0.00170475290291542, -0.000682767885032995, 0);
//[0.006636837611004,0.050240447649195] [0.006681263320267,0.003130367429418] //左相机K1,K2,P1,P2,K3
/*事先标定好的右相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixR = (Mat_<double>(3, 3) << 489.689437035131, -0.582092467855873, 299.695314139699,
0, 489.272059352339, 212.644573601761,
0, 0, 1); //右相机畸变参数
/*
417.417985082506 0 0
0.498638151824367 419.795432389420 0
309.903372309072 236.256106972796 1
*/ //2
Mat distCoeffR = (Mat_<double>(5, 1) << 0.100993814523132, -0.0586344661784253, -0.00121091192337629, -0.00108736536874651, 0);
//[-0.038407383078874,0.236392800301615] [0.004121779274885,0.002296129959664]//右相机K1,K2,P1,P2,K3
Mat T = (Mat_<double>(3, 1) << -45.6655434838876, 0.0452737841151481, 0.422970847106132);//T平移向量
//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数 TranslationOfCamera2
//Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量，对应matlab om参数 我
Mat rec = (Mat_<double>(3, 3) << 0.999999575725951, 0.000388484222917634, 0.000835241238359436,
-0.000388657271549596, 0.999999903041699, 0.000207031685706618,
-0.000835160728832328, -0.000207356220449212, 0.999999629754909); //rec旋转向量，对应matlab om参数 我
//RotationOfCamera2
/* 0.999341122700880 0.000660748031451783 -0.0362888948713456
-0.00206388651740061 0.999250989651683 -0.0386419468010579
0.0362361815232777 0.0386913826603732 0.998593969567432 */
//Mat T = (Mat_<double>(3, 1) << -48.4, 0.241, -0.0344);//T平移向量
//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数
//Mat rec = (Mat_<double>(3, 1) << -0.039, -0.04658, 0.00106);//rec旋转向量，对应matlab om参数 倬华
Mat R;//R 旋转矩阵
/*****立体匹配*****/
void stereo_match(int, void*)
{
bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜
bm->setROI1(validROIL);
bm->setROI2(validROIR);
bm->setPreFilterCap(31);
bm->setMinDisparity(0); //最小视差，默认值为0, 可以是负值，int型
bm->setNumDisparities(numDisparities * 16 + 16);//视差窗口，即最大视差值与最小视差值之差,窗口大小必须是16的整数倍，int型
bm->setTextureThreshold(10);
bm->setUniquenessRatio(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
bm->setSpeckleWindowSize(100);
bm->setSpeckleRange(32);
bm->setDisp12MaxDiff(-1);
Mat disp, disp8;
bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
reprojectImageTo3D(disp, xyz, Q, true); //在实际求距离时，ReprojectTo3D出来的X / W, Y / W, Z / W都要乘以16(也就是W除以16)，才能得到正确的三维坐标信息。
xyz = xyz * 16;
imshow("disparity", disp8);
}
/描述：鼠标操作回调/
static void onMouse(int event, int x, int y, int, void*)
{
if (selectObject)
{
selection.x = MIN(x, origin.x);
selection.y = MIN(y, origin.y);
selection.width = std::abs(x - origin.x);
selection.height = std::abs(y - origin.y);
}
switch (event)
{
case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
origin = Point(x, y);
selection = Rect(x, y, 0, 0);
selectObject = true;
//cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
point3 = xyz.at<Vec3f>(origin);
point3[0];
//cout << "point3[0]:" << point3[0] << "point3[1]:" << point3[1] << "point3[2]:" << point3[2]<<endl;
cout << "世界坐标：" << endl;
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
d = point3[0] * point3[0] + point3[1] * point3[1] + point3[2] * point3[2];
d = sqrt(d); //mm
// cout << "距离是:" << d << "mm" << endl;
d = d / 10.0; //cm
cout << "距离是:" << d << "cm" << endl;
// d = d/1000.0; //m
// cout << "距离是:" << d << "m" << endl;
break;
case EVENT_LBUTTONUP: //鼠标左按钮释放的事件
selectObject = false;
if (selection.width > 0 && selection.height > 0)
break;
}
}
/主函数/
int main()
{
/*
立体校正
*/
Rodrigues(rec, R); //Rodrigues变换
stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY,
0, imageSize, &validROIL, &validROIR);
initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pl, imageSize, CV_32FC1, mapLx, mapLy);
initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
/*
打开摄像头
*/
VideoCapture cap;
cap.open(0); //打开相机，电脑自带摄像头一般编号为0，外接摄像头编号为1，主要是在设备管理器中查看自己摄像头的编号。
cap.set(CV_CAP_PROP_FRAME_WIDTH, 2560); //设置捕获视频的宽度
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 720); //设置捕获视频的高度
if (!cap.isOpened()) //判断是否成功打开相机
{
cout << "摄像头打开失败!" << endl;
return -1;
}
Mat frame, frame_L, frame_R;
cap >> frame; //从相机捕获一帧图像
cout << "Painted ImageL" << endl;
cout << "Painted ImageR" << endl;
while (1) {
double fScale = 0.5; //定义缩放系数，对2560*720图像进行缩放显示（2560*720图像过大，液晶屏分辨率较小时，需要缩放才可完整显示在屏幕）
Size dsize = Size(frame.cols*fScale, frame.rows*fScale);
Mat imagedst = Mat(dsize, CV_32S);
resize(frame, imagedst, dsize);
char image_left[200];
char image_right[200];
frame_L = imagedst(Rect(0, 0, 640, 360)); //获取缩放后左Camera的图像
// namedWindow("Video_L", 1);
// imshow("Video_L", frame_L);
frame_R = imagedst(Rect(640, 0, 640, 360)); //获取缩放后右Camera的图像
// namedWindow("Video_R", 2);
// imshow("Video_R", frame_R);
cap >> frame;
/*
读取图片
*/
//rgbImageL = imread("image_left_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(frame_L, grayImageL, CV_BGR2GRAY);
//rgbImageR = imread("image_right_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(frame_R, grayImageR, CV_BGR2GRAY);
// imshow("ImageL Before Rectify", grayImageL);
// imshow("ImageR Before Rectify", grayImageR);
/*
经过remap之后，左右相机的图像已经共面并且行对准了
*/
remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);
/*
把校正结果显示出来
*/
Mat rgbRectifyImageL, rgbRectifyImageR;
cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR); //伪彩色图
cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR);
//单独显示
//rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8);
//rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8);
// imshow("ImageL After Rectify", rgbRectifyImageL);
// imshow("ImageR After Rectify", rgbRectifyImageR);
//显示在同一张图上
Mat canvas;
double sf;
int w, h;
sf = 600. / MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width * sf);
h = cvRound(imageSize.height * sf);
canvas.create(h, w * 2, CV_8UC3); //注意通道
//左图像画到画布上
Mat canvasPart = canvas(Rect(w * 0, 0, w, h)); //得到画布的一部分
resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA); //把图像缩放到跟canvasPart一样大小
Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf), //获得被截取的区域
cvRound(validROIL.width*sf), cvRound(validROIL.height*sf));
//rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8); //画上一个矩形
// cout << "Painted ImageL" << endl;
//右图像画到画布上
canvasPart = canvas(Rect(w, 0, w, h)); //获得画布的另一部分
resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR);
Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf),
cvRound(validROIR.width * sf), cvRound(validROIR.height * sf));
//rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8);
// cout << "Painted ImageR" << endl;
//画上对应的线条
for (int i = 0; i < canvas.rows; i += 16)
line(canvas, Point(0, i), Point(canvas.cols, i), Scalar(0, 255, 0), 1, 8);
imshow("rectified", canvas);
/*
立体匹配
*/
namedWindow("disparity", CV_WINDOW_AUTOSIZE);
// 创建SAD窗口 Trackbar
createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match);
// 创建视差唯一性百分比窗口 Trackbar
createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match);
// 创建视差窗口 Trackbar
createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match);
//鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0)
setMouseCallback("disparity", onMouse, 0);
stereo_match(0, 0);
waitKey(10);
} //wheil
return 0;
}
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